Small-subspace expansion #
A subspace U of Fin M → K⸨X⸩ supported in the first N coordinates, of dimension
1 ≤ k ≤ B, expands under the reverse-Taylor tests at w distinct points of a characteristic
zero field K: any common target subspace containing all w images has dimension greater than
(w - 1) * k, as soon as M = N + 2 w³ B. The proof combines the order reversal rank
inequality of LaurentReduction with the Wronskian order bound of Expansion.
Main definitions #
SmallExpansion.polynomialMap: coefficient vectors as polynomials of degree< M.SmallExpansion.polynomialSpace: the polynomial subspace of a coefficient subspace.SmallExpansion.taylorCoordinates,SmallExpansion.reverseTaylorCoordinates: Taylor coordinates at a point, in increasing and in reversed order.SmallExpansion.testMap: the reverse-Taylor test at a point, weighting thej-th Taylor coordinate byX ^ j.
Main results #
SmallExpansion.order_reversal_sum: summed order reversal at a single point.SmallExpansion.small_expansion: the expansion inequality(w - 1) * finrank U < finrank W.
References #
Coefficient vectors of length M as polynomials of degree less than M.
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The polynomial subspace corresponding to a subspace of coefficient vectors.
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The reverse-Taylor test on bounded-degree polynomials multiplies each Taylor coefficient by t^a and places it in output degree M-1-a.
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Kernel of the low Taylor projection is precisely the vanishing filtration.
The output-side low reverse coordinates mean high ordinary Taylor order.
The rank form of order reversal, at every threshold.
Summation of order reversal, without selecting incompatible auxiliary subspaces: all inequalities concern the fixed filtration of the same W.
Small-subspace expansion in monomial coordinates in a common output space.
The input subspace is restricted to degrees < N, not changed by padding.